The present application relates to the field of image modalities. It finds particular application to image modalities that employ photon counting techniques (e.g., such as image modalities that employ x-ray and/or gamma radiation) and/or to spatial uniformity correction and/or to modulated transfer function (MTF) improvement (e.g., optimization) for image modalities that implement photon counting. For example, medical, security, and/or industrial applications may utilize a computed tomography (CT) scanner comprising photon counting pixels to count the number of photons that are detected by respective pixels and, based upon the number of photons detected by respective pixels, one or more images providing a two-dimensional and/or three-dimensional representation of an object under examination may be generated therefrom.
Today, CT and other image modalities (e.g., single-photon emission computed tomography (SPECT), mammography, digital radiography, etc.) are useful to provide information, or images, of interior aspects of an object under examination. Generally, the object is exposed to radiation comprising photons (e.g., such as x-rays, gamma rays, etc.), and an image(s) is formed based upon the radiation absorbed by the interior aspects of the object, or rather an amount of photons that is able to pass through the object. Generally speaking, highly dense aspects of the object (e.g., or aspects of the object having a composition comprised of higher atomic number elements) absorb more radiation than less dense aspects, and thus an aspect having a higher density, such as a bone or metal, for example, will be apparent when surrounded by less dense aspects, such as muscle or clothing.
Radiographic image modalities generally comprise, among other things, a detector array comprised of a plurality of pixels that are respectively configured to convert radiation that has traversed the object into signals that may be processed to produce the image(s). The pixels are typically one of “charge integrating” and “photon counting” type pixels (e.g., the image modality operates in charge integration mode, photon counting mode, or both).
Charge integrating type pixels (e.g., pixels comprising charge integrating channels) are configured to convert energy into signals (e.g., current or voltage signals) that are proportional to an incoming photon flux rate. Respective signals may then be integrated over a time period (e.g., referred to herein as a measurement interval), sampled, and digitized. While this type of pixel is widely used, there are several drawbacks to such pixels. For example, charge integrating type pixels are generally not able to provide feedback as to the number and/or energy of photons detected. Moreover, there is a lower limit of detection defined by noise in the pixel such that a pixel with little to no incident radiation may produce some signal due to thermal and/or analog read noise (e.g., produced by the detector array and/or readout components). It will be appreciated that as a result of this lower limit, the dose of radiation that is applied to an object under examination is generally greater than the dose of radiation that may be applied to the object if the pixels are of a photon counting type.
Photon counting type pixels (e.g., pixels comprising photon counting channels) are configured to convert energy into signals that are proportional to the energy of a detected photon (e.g., also referred to herein as a radiation event). Thus, ideally, signals produced by respective pixels generally comprise one or more current and/or voltage pulses, for example, respectively associated with a single radiation event. A controller may then be used to determine the location and energy of respective radiation events, accumulate the radiation events occurring during a measurement interval, digitize the information, and process the digital information to form an image, for example. It will be appreciated to those skilled in the art that there are numerous advantages to photon counting type pixels over charge integrating type pixels. For example, the counting of photons is essentially noise free (e.g., apart from inherent photon shot noise). Therefore, a lower dose of radiation may be applied to the object under examination. Moreover, photon counting type pixels generally allow for energy (e.g., or wavelength) discrimination. Therefore, images indicative of different energy levels of radiation may be obtained at the same time, for example.
While photon counting type pixels have numerous advantages over charge integrating type pixels, variations in the pixel area of respective photon counting type pixels (e.g., caused by manufacturing defects, electric field distortion, etc.) can be a significant source of spatial non-uniformity (e.g., causing intensity variations in resulting images). A pixel with a larger area tends to detect more photons to the detriment of one or more pixels adjacent to the pixel with the larger area. Conventionally, these variations in pixel area have been corrected by what is referred to in the art as a gain correction. As part of the gain correction, a calibration is performed and a multiplicative factor is found for respective pixels based on a ratio of the response of a pixel to which the multiplication factor is applied relative to an average response of neighboring pixels. For example, a pixel that counts fewer photons during the calibration (e.g., when a uniform dose is exposed to substantially all pixels of the detector array) than neighboring pixels may have a multiplicative factor of greater than one applied to it (e.g., such that the number of photons detected by the pixel during an examination is multiplied by the same multiplicative factor greater than 1).
As described above, the predominate (e.g., substantially only) source of noise in a photon counting detector array is expected to come from the statistical process of counting photons. The more photons detected by a pixel, the better the signal-to-noise ratio, which is equal to the square root of the number of photons counted by the pixel. By applying the multiplicative factor to the signal of a pixel, it will be appreciated that a similar multiplicative factor is also applied to the statistical noise of the pixel, causing the total noise of the pixel to increase or decrease (e.g., depending upon whether the multiplicative factor is greater than or less than 1) but the signal-to-noise ratio of the pixel to remain the same. Therefore, despite the gain correction, there may remain a variation in the signal-to-noise ratio of pixels neighboring one another, which may affect (e.g., reduce or degrade) resulting images.